Generative Models of Multichannel Data from a Single Example—Application to Dust Emission

Abstract

Abstract The quest for primordial B-modes in the cosmic microwave background has emphasized the need for refined models of the Galactic dust foreground. Here we aim at building a realistic statistical model of the multifrequency dust emission from a single example. We introduce a generic methodology relying on microcanonical gradient descent models conditioned by an extended family of wavelet phase harmonic (WPH) statistics. To tackle the multichannel aspect of the data, we define cross-WPH statistics, quantifying non-Gaussian correlations between maps. Our data-driven methodology could apply to various contexts, and we have updated the software PyWPH, on which this work relies, accordingly. Applying this to dust emission maps built from a magnetohydrodynamics simulation, we construct and assess two generative models: (1) a (I, E, B) multi-observable input, and (2) a { I ν } ν multifrequency input. The samples exhibit consistent features compared to the original maps. A statistical analysis of model 1 shows that the power spectra, distributions of pixels, and Minkowski functionals are captured to a good extent. We analyze model 2 by fitting the spectral energy distribution (SED) of both the synthetic and original maps with a modified blackbody (MBB) law. The maps are equally well fitted, and a comparison of the MBB parameters shows that our model succeeds in capturing the spatial variations of the SED from the data. Besides the perspectives of this work for dust emission modeling, the introduction of cross-WPH statistics opens a new avenue to characterize non-Gaussian interactions across different maps, which we believe will be fruitful for astrophysics.